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UNINA9910702116903321 |
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Titolo |
The Effects of digital sampling rate and bit quantization on passive auditory sonar target detection performance [[electronic resource] /] / J.S. Russotti ... [and others] |
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Pubbl/distr/stampa |
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Groton, CT : , : Naval Submarine Medical Research Laboratory, , [1993] |
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Descrizione fisica |
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1 online resource (iii, 10 pages, 2 unnumbered pages) : illustrations |
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Collana |
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Altri autori (Persone) |
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Soggetti |
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Signal processing - Digital techniques - Evaluation |
Auditory perception - Evaluation |
Sonar |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from title screen (viewed Oct. 3, 2012). |
"4 February 1993." |
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Nota di bibliografia |
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Includes bibliographical references (pages 9-10). |
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2. |
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UNINA9910798003903321 |
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Autore |
Mukherjee Sudipta |
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Titolo |
F# for machine learning essentials : get up and running with machine learning with F# in a fun and functional way / / Sudipta Mukherjee ; foreword by Dr. Ralf Herbrich, director of machine learning science at Amazon |
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Pubbl/distr/stampa |
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Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 |
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©2016 |
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ISBN |
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Edizione |
[1.] |
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Descrizione fisica |
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1 online resource (194 p.) |
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Collana |
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Community Experience Distilled |
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Disciplina |
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Soggetti |
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F# (Computer program language) |
Machine learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning; Objective; Getting in touch; Different areas where machine learning is being used; Why use F#?; Supervised machine learning; Training and test dataset/corpus; Some motivating real life examples of supervised learning; Nearest Neighbour algorithm (a.k.a k-NN algorithm); Distance metrics; Decision tree algorithms; Unsupervised learning; Machine learning frameworks; Machine learning for fun and profit |
Recognizing handwritten digits - your ""Hello World"" ML programHow does this work?; Summary; Chapter 2: Linear Regression; Objective; Different types of linear regression algorithms; APIs used; Math.NET Numerics for F# 3.7.0; Getting Math.NET; Experimenting with Math.NET; The basics of matrices and vectors (a short and sweet refresher); Creating a vector; Creating a matrix; Finding the transpose of a matrix; Finding the inverse of a matrix; Trace of a matrix; QR decomposition of a matrix; SVD of a matrix; Linear regression method of least square |
Finding linear regression coefficients using F#Finding the linear regression coefficients using Math.NET; Putting it together with Math.NET and FsPlot; Multiple linear regression; Multiple linear regression |
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and variations using Math.NET; Weighted linear regression; Plotting the result of multiple linear regression; Ridge regression; Multivariate multiple linear regression; Feature scaling; Summary; Chapter 3: Classification Techniques; Objective; Different classification algorithms you will learn; Some interesting things you can do; Binary classification using k-NN; How does it work? |
Finding cancerous cells using k-NN: a case studyUnderstanding logistic regression ; The sigmoid function chart; Binary classification using logistic regression (using Accord.NET); Multiclass classification using logistic regression; How does it work?; Multiclass classification using decision trees; Obtaining and using WekaSharp; How does it work?; Predicting a traffic jam using a decision tree: a case study; Challenge yourself!; Summary; Chapter 4: Information Retrieval; Objective; Different IR algorithms you will learn; What interesting things can you do? |
Information retrieval using tf-idfMeasures of similarity; Generating a PDF from a histogram; Minkowski family; L1 family; Intersection family; Inner Product family; Fidelity family or squared-chord family; Squared L2 family; Shannon's Entropy family; Similarity of asymmetric binary attributes; Some example usages of distance metrics; Finding similar cookies using asymmetric binary similarity measures; Grouping/clustering color images based on Canberra distance; Summary; Chapter 5: Collaborative Filtering; Objective; Different classification algorithms you will learn |
Vocabulary of collaborative filtering |
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3. |
Record Nr. |
UNINA9910132168803321 |
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Titolo |
Journal of computational methods in physics |
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Pubbl/distr/stampa |
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New York, NY, : Hindawi Publishing Corporation |
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ISSN |
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Descrizione fisica |
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Soggetti |
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Physics - Data processing |
Mathematical physics - Data processing |
Periodicals. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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